tar_download {tarchetypes} | R Documentation |
Target that downloads URLs.
Description
Create a target that downloads file from one or more URLs
and automatically reruns when the remote data changes
(according to the ETags or last-modified time stamps).
Usage
tar_download(
name,
urls,
paths,
method = NULL,
quiet = TRUE,
mode = "w",
cacheOK = TRUE,
extra = NULL,
headers = NULL,
iteration = targets::tar_option_get("iteration"),
error = targets::tar_option_get("error"),
memory = targets::tar_option_get("memory"),
garbage_collection = targets::tar_option_get("garbage_collection"),
deployment = targets::tar_option_get("deployment"),
priority = targets::tar_option_get("priority"),
resources = targets::tar_option_get("resources"),
storage = targets::tar_option_get("storage"),
retrieval = targets::tar_option_get("retrieval"),
cue = targets::tar_option_get("cue"),
description = targets::tar_option_get("description")
)
Arguments
name |
Symbol, name of the target. A target
name must be a valid name for a symbol in R, and it
must not start with a dot. Subsequent targets
can refer to this name symbolically to induce a dependency relationship:
e.g. tar_target(downstream_target, f(upstream_target)) is a
target named downstream_target which depends on a target
upstream_target and a function f() . In addition, a target's
name determines its random number generator seed. In this way,
each target runs with a reproducible seed so someone else
running the same pipeline should get the same results,
and no two targets in the same pipeline share the same seed.
(Even dynamic branches have different names and thus different seeds.)
You can recover the seed of a completed target
with tar_meta(your_target, seed) and run tar_seed_set()
on the result to locally recreate the target's initial RNG state.
|
urls |
Character vector of URLs to track and download.
Must be known and declared before the pipeline runs.
|
paths |
Character vector of local file paths to
download each of the URLs.
Must be known and declared before the pipeline runs.
|
method |
Method to be used for downloading files. Current
download methods are "internal" , "libcurl" ,
"wget" , "curl" and "wininet" (Windows
only), and there is a value "auto" : see ‘Details’ and
‘Note’.
The method can also be set through the option
"download.file.method" : see options() .
|
quiet |
If TRUE , suppress status messages (if any), and
the progress bar.
|
mode |
character. The mode with which to write the file. Useful
values are "w" , "wb" (binary), "a" (append) and
"ab" . Not used for methods "wget" and "curl" .
See also ‘Details’, notably about using "wb" for Windows.
|
cacheOK |
logical. Is a server-side cached value acceptable?
|
|
character vector of additional command-line arguments for
the "wget" and "curl" methods.
|
|
named character vector of additional HTTP headers to
use in HTTP[S] requests. It is ignored for non-HTTP[S] URLs. The
User-Agent header taken from the HTTPUserAgent option
(see options ) is automatically used as the first header.
|
iteration |
Character of length 1, name of the iteration mode
of the target. Choices:
-
"vector" : branching happens with vctrs::vec_slice() and
aggregation happens with vctrs::vec_c() .
-
"list" , branching happens with [[]] and aggregation happens with
list() .
-
"group" : dplyr::group_by() -like functionality to branch over
subsets of a non-dynamic data frame.
For iteration = "group" , the target must not by dynamic
(the pattern argument of tar_target() must be left NULL ).
The target's return value must be a data
frame with a special tar_group column of consecutive integers
from 1 through the number of groups. Each integer designates a group,
and a branch is created for each collection of rows in a group.
See the tar_group() function to see how you can
create the special tar_group column with dplyr::group_by() .
|
error |
Character of length 1, what to do if the target
stops and throws an error. Options:
-
"stop" : the whole pipeline stops and throws an error.
-
"continue" : the whole pipeline keeps going.
-
"abridge" : any currently running targets keep running,
but no new targets launch after that.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
-
"null" : The errored target continues and returns NULL .
The data hash is deliberately wrong so the target is not
up to date for the next run of the pipeline.
|
memory |
Character of length 1, memory strategy.
If "persistent" , the target stays in memory
until the end of the pipeline (unless storage is "worker" ,
in which case targets unloads the value from memory
right after storing it in order to avoid sending
copious data over a network).
If "transient" , the target gets unloaded
after every new target completes.
Either way, the target gets automatically loaded into memory
whenever another target needs the value.
For cloud-based dynamic files
(e.g. format = "file" with repository = "aws" ),
this memory strategy applies to the
temporary local copy of the file:
"persistent" means it remains until the end of the pipeline
and is then deleted,
and "transient" means it gets deleted as soon as possible.
The former conserves bandwidth,
and the latter conserves local storage.
|
garbage_collection |
Logical, whether to run base::gc()
just before the target runs.
|
deployment |
Character of length 1. If deployment is
"main" , then the target will run on the central controlling R process.
Otherwise, if deployment is "worker" and you set up the pipeline
with distributed/parallel computing, then
the target runs on a parallel worker. For more on distributed/parallel
computing in targets , please visit
https://books.ropensci.org/targets/crew.html.
|
priority |
Numeric of length 1 between 0 and 1. Controls which
targets get deployed first when multiple competing targets are ready
simultaneously. Targets with priorities closer to 1 get dispatched earlier
(and polled earlier in tar_make_future() ).
|
resources |
Object returned by tar_resources()
with optional settings for high-performance computing
functionality, alternative data storage formats,
and other optional capabilities of targets .
See tar_resources() for details.
|
storage |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future() .
Must be one of the following values:
-
"main" : the target's return value is sent back to the
host machine and saved/uploaded locally.
-
"worker" : the worker saves/uploads the value.
-
"none" : almost never recommended. It is only for
niche situations, e.g. the data needs to be loaded
explicitly from another language. If you do use it,
then the return value of the target is totally ignored
when the target ends, but
each downstream target still attempts to load the data file
(except when retrieval = "none" ).
If you select storage = "none" , then
the return value of the target's command is ignored,
and the data is not saved automatically.
As with dynamic files (format = "file" ) it is the
responsibility of the user to write to
the data store from inside the target.
The distinguishing feature of storage = "none"
(as opposed to format = "file" )
is that in the general case,
downstream targets will automatically try to load the data
from the data store as a dependency. As a corollary, storage = "none"
is completely unnecessary if format is "file" .
|
retrieval |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future() .
Must be one of the following values:
-
"main" : the target's dependencies are loaded on the host machine
and sent to the worker before the target runs.
-
"worker" : the worker loads the targets dependencies.
-
"none" : the dependencies are not loaded at all.
This choice is almost never recommended. It is only for
niche situations, e.g. the data needs to be loaded
explicitly from another language.
|
cue |
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
|
description |
Character of length 1, a custom free-form human-readable
text description of the target. Descriptions appear as target labels
in functions like tar_manifest() and tar_visnetwork() ,
and they let you select subsets of targets for the names argument of
functions like tar_make() . For example,
tar_manifest(names = tar_described_as(starts_with("survival model")))
lists all the targets whose descriptions start with the character
string "survival model" .
|
Details
tar_download()
creates a pair of targets, one upstream
and one downstream. The upstream target uses format = "url"
(see targets::tar_target()
) to track files at one or more URLs,
and automatically invalidate the target if the ETags
or last-modified time stamps change. The downstream target
depends on the upstream one, downloads the files,
and tracks them using format = "file"
.
Value
A list of two target objects, one upstream and one downstream.
The upstream one watches a URL for changes, and the downstream one
downloads it.
See the "Target objects" section for background.
Target objects
Most tarchetypes
functions are target factories,
which means they return target objects
or lists of target objects.
Target objects represent skippable steps of the analysis pipeline
as described at https://books.ropensci.org/targets/.
Please read the walkthrough at
https://books.ropensci.org/targets/walkthrough.html
to understand the role of target objects in analysis pipelines.
For developers,
https://wlandau.github.io/targetopia/contributing.html#target-factories
explains target factories (functions like this one which generate targets)
and the design specification at
https://books.ropensci.org/targets-design/
details the structure and composition of target objects.
See Also
Other targets with custom invalidation rules:
tar_change()
,
tar_force()
,
tar_skip()
Examples
if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
list(
tarchetypes::tar_download(
x,
urls = c("https://httpbin.org/etag/test", "https://r-project.org"),
paths = c("downloaded_file_1", "downloaded_file_2")
)
)
})
targets::tar_make()
targets::tar_read(x)
})
}
[Package
tarchetypes version 0.9.0
Index]